Remove <master> from examples + update usages

examples/src/main/python/als.py

@@ -46,15 +46,15 @@ def update(i, vec, mat, ratings):
     return np.linalg.solve(XtX, Xty)
 
 if __name__ == "__main__":
-    if len(sys.argv) < 2:
-        print >> sys.stderr, "Usage: als <master> <M> <U> <F> <iters> <slices>"
-        exit(-1)
-    sc = SparkContext(sys.argv[1], "PythonALS", pyFiles=[realpath(__file__)])
-    M = int(sys.argv[2]) if len(sys.argv) > 2 else 100
-    U = int(sys.argv[3]) if len(sys.argv) > 3 else 500
-    F = int(sys.argv[4]) if len(sys.argv) > 4 else 10
-    ITERATIONS = int(sys.argv[5]) if len(sys.argv) > 5 else 5
-    slices = int(sys.argv[6]) if len(sys.argv) > 6 else 2
+    """
+        Usage: als [M] [U] [F] [iterations] [slices]"
+    """
+    sc = SparkContext(appName="PythonALS", pyFiles=[realpath(__file__)])
+    M = int(sys.argv[1]) if len(sys.argv) > 1 else 100
+    U = int(sys.argv[2]) if len(sys.argv) > 2 else 500
+    F = int(sys.argv[3]) if len(sys.argv) > 3 else 10
+    ITERATIONS = int(sys.argv[4]) if len(sys.argv) > 4 else 5
+    slices = int(sys.argv[5]) if len(sys.argv) > 5 else 2
 
     print "Running ALS with M=%d, U=%d, F=%d, iters=%d, slices=%d\n" % \
             (M, U, F, ITERATIONS, slices)

examples/src/main/python/kmeans.py

@@ -45,14 +45,14 @@ def closestPoint(p, centers):
 
 
 if __name__ == "__main__":
-    if len(sys.argv) < 5:
-        print >> sys.stderr, "Usage: kmeans <master> <file> <k> <convergeDist>"
+    if len(sys.argv) != 4:
+        print >> sys.stderr, "Usage: kmeans <file> <k> <convergeDist>"
         exit(-1)
-    sc = SparkContext(sys.argv[1], "PythonKMeans")
-    lines = sc.textFile(sys.argv[2])
+    sc = SparkContext(appName="PythonKMeans")
+    lines = sc.textFile(sys.argv[1])
     data = lines.map(parseVector).cache()
-    K = int(sys.argv[3])
-    convergeDist = float(sys.argv[4])
+    K = int(sys.argv[2])
+    convergeDist = float(sys.argv[3])
 
     kPoints = data.takeSample(False, K, 1)
     tempDist = 1.0

examples/src/main/python/logistic_regression.py

@@ -47,12 +47,12 @@ def readPointBatch(iterator):
     return [matrix]
 
 if __name__ == "__main__":
-    if len(sys.argv) != 4:
-        print >> sys.stderr, "Usage: logistic_regression <master> <file> <iters>"
+    if len(sys.argv) != 3:
+        print >> sys.stderr, "Usage: logistic_regression <file> <iterations>"
         exit(-1)
-    sc = SparkContext(sys.argv[1], "PythonLR", pyFiles=[realpath(__file__)])
-    points = sc.textFile(sys.argv[2]).mapPartitions(readPointBatch).cache()
-    iterations = int(sys.argv[3])
+    sc = SparkContext(appName="PythonLR", pyFiles=[realpath(__file__)])
+    points = sc.textFile(sys.argv[1]).mapPartitions(readPointBatch).cache()
+    iterations = int(sys.argv[2])
 
     # Initialize w to a random value
     w = 2 * np.random.ranf(size=D) - 1

examples/src/main/python/mllib/kmeans.py

@@ -33,12 +33,12 @@ def parseVector(line):
 
 
 if __name__ == "__main__":
-    if len(sys.argv) < 4:
-        print >> sys.stderr, "Usage: kmeans <master> <file> <k>"
+    if len(sys.argv) != 3:
+        print >> sys.stderr, "Usage: kmeans <file> <k>"
         exit(-1)
-    sc = SparkContext(sys.argv[1], "KMeans")
-    lines = sc.textFile(sys.argv[2])
+    sc = SparkContext(appName="KMeans")
+    lines = sc.textFile(sys.argv[1])
     data = lines.map(parseVector)
-    k = int(sys.argv[3])
+    k = int(sys.argv[2])
     model = KMeans.train(data, k)
     print "Final centers: " + str(model.clusterCenters)

examples/src/main/python/mllib/logistic_regression.py

@@ -39,12 +39,12 @@ def parsePoint(line):
 
 
 if __name__ == "__main__":
-    if len(sys.argv) != 4:
-        print >> sys.stderr, "Usage: logistic_regression <master> <file> <iters>"
+    if len(sys.argv) != 3:
+        print >> sys.stderr, "Usage: logistic_regression <file> <iterations>"
         exit(-1)
-    sc = SparkContext(sys.argv[1], "PythonLR")
-    points = sc.textFile(sys.argv[2]).map(parsePoint)
-    iterations = int(sys.argv[3])
+    sc = SparkContext(appName="PythonLR")
+    points = sc.textFile(sys.argv[1]).map(parsePoint)
+    iterations = int(sys.argv[2])
     model = LogisticRegressionWithSGD.train(points, iterations)
     print "Final weights: " + str(model.weights)
     print "Final intercept: " + str(model.intercept)

examples/src/main/python/pagerank.py

@@ -36,19 +36,19 @@ def parseNeighbors(urls):
 
 
 if __name__ == "__main__":
-    if len(sys.argv) < 3:
-        print >> sys.stderr, "Usage: pagerank <master> <file> <number_of_iterations>"
+    if len(sys.argv) != 3:
+        print >> sys.stderr, "Usage: pagerank <file> <iterations>"
         exit(-1)
 
     # Initialize the spark context.
-    sc = SparkContext(sys.argv[1], "PythonPageRank")
+    sc = SparkContext(appName="PythonPageRank")
 
     # Loads in input file. It should be in format of:
     #     URL         neighbor URL
     #     URL         neighbor URL
     #     URL         neighbor URL
     #     ...
-    lines = sc.textFile(sys.argv[2], 1)
+    lines = sc.textFile(sys.argv[1], 1)
 
     # Loads all URLs from input file and initialize their neighbors.
     links = lines.map(lambda urls: parseNeighbors(urls)).distinct().groupByKey().cache()
@@ -57,7 +57,7 @@ def parseNeighbors(urls):
     ranks = links.map(lambda (url, neighbors): (url, 1.0))
 
     # Calculates and updates URL ranks continuously using PageRank algorithm.
-    for iteration in xrange(int(sys.argv[3])):
+    for iteration in xrange(int(sys.argv[2])):
         # Calculates URL contributions to the rank of other URLs.
         contribs = links.join(ranks).flatMap(lambda (url, (urls, rank)):
             computeContribs(urls, rank))

examples/src/main/python/pi.py

@@ -23,11 +23,11 @@
 
 
 if __name__ == "__main__":
-    if len(sys.argv) == 1:
-        print >> sys.stderr, "Usage: pi <master> [<slices>]"
-        exit(-1)
-    sc = SparkContext(sys.argv[1], "PythonPi")
-    slices = int(sys.argv[2]) if len(sys.argv) > 2 else 2
+    """
+        Usage: pi [slices]
+    """
+    sc = SparkContext(appName="PythonPi")
+    slices = int(sys.argv[1]) if len(sys.argv) > 1 else 2
     n = 100000 * slices
     def f(_):
         x = random() * 2 - 1

examples/src/main/python/sort.py

@@ -21,11 +21,11 @@
 
 
 if __name__ == "__main__":
-    if len(sys.argv) < 3:
-        print >> sys.stderr, "Usage: sort <master> <file>"
+    if len(sys.argv) != 2:
+        print >> sys.stderr, "Usage: sort <file>"
         exit(-1)
-    sc = SparkContext(sys.argv[1], "PythonSort")
-    lines = sc.textFile(sys.argv[2], 1)
+    sc = SparkContext(appName="PythonSort")
+    lines = sc.textFile(sys.argv[1], 1)
     sortedCount = lines.flatMap(lambda x: x.split(' ')) \
                   .map(lambda x: (int(x), 1)) \
                   .sortByKey(lambda x: x)

examples/src/main/python/transitive_closure.py

@@ -36,11 +36,11 @@ def generateGraph():
 
 
 if __name__ == "__main__":
-    if len(sys.argv) == 1:
-        print >> sys.stderr, "Usage: transitive_closure <master> [<slices>]"
-        exit(-1)
-    sc = SparkContext(sys.argv[1], "PythonTransitiveClosure")
-    slices = int(sys.argv[2]) if len(sys.argv) > 2 else 2
+    """
+        Usage: transitive_closure [slices]
+    """
+    sc = SparkContext(appName="PythonTransitiveClosure")
+    slices = int(sys.argv[1]) if len(sys.argv) > 1 else 2
     tc = sc.parallelize(generateGraph(), slices).cache()
 
     # Linear transitive closure: each round grows paths by one edge,

examples/src/main/python/wordcount.py

@@ -22,11 +22,11 @@
 
 
 if __name__ == "__main__":
-    if len(sys.argv) < 3:
-        print >> sys.stderr, "Usage: wordcount <master> <file>"
+    if len(sys.argv) != 2:
+        print >> sys.stderr, "Usage: wordcount <file>"
         exit(-1)
-    sc = SparkContext(sys.argv[1], "PythonWordCount")
-    lines = sc.textFile(sys.argv[2], 1)
+    sc = SparkContext(appName="PythonWordCount")
+    lines = sc.textFile(sys.argv[1], 1)
     counts = lines.flatMap(lambda x: x.split(' ')) \
                   .map(lambda x: (x, 1)) \
                   .reduceByKey(add)